{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 全卷积网络（FCN）\n",
    "\n",
    "上一节介绍了，我们可以基于语义分割对图像中的每个像素进行类别预测。全卷积网络（fully convolutional network，FCN）采用卷积神经网络实现了从图像像素到像素类别的变换 [1]。与之前介绍的卷积神经网络有所不同，全卷积网络通过转置卷积（transposed convolution）层将中间层特征图的高和宽变换回输入图像的尺寸，从而令预测结果与输入图像在空间维（高和宽）上一一对应：给定空间维上的位置，通道维的输出即该位置对应像素的类别预测。\n",
    "\n",
    "我们先导入实验所需的包或模块，然后解释什么是转置卷积层。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "2"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import d2lzh as d2l\n",
    "from mxnet import gluon, image, init, nd\n",
    "from mxnet.gluon import data as gdata, loss as gloss, model_zoo, nn\n",
    "import numpy as np\n",
    "import sys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 转置卷积层\n",
    "\n",
    "顾名思义，转置卷积层得名于矩阵的转置操作。事实上，卷积运算还可以通过矩阵乘法来实现。在下面的例子中，我们定义高和宽分别为4的输入`X`，以及高和宽分别为3的卷积核`K`。打印二维卷积运算的输出以及卷积核。可以看到，输出的高和宽分别为2。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(\n",
       " [[[[348. 393.]\n",
       "    [528. 573.]]]]\n",
       " <NDArray 1x1x2x2 @cpu(0)>, \n",
       " [[[[1. 2. 3.]\n",
       "    [4. 5. 6.]\n",
       "    [7. 8. 9.]]]]\n",
       " <NDArray 1x1x3x3 @cpu(0)>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = nd.arange(1, 17).reshape((1, 1, 4, 4))\n",
    "K = nd.arange(1, 10).reshape((1, 1, 3, 3))\n",
    "conv = nn.Conv2D(channels=1, kernel_size=3)\n",
    "conv.initialize(init.Constant(K))\n",
    "conv(X), K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们将卷积核`K`改写成含有大量零元素的稀疏矩阵`W`，即权重矩阵。权重矩阵的形状为(4, 16)，其中的非零元素来自卷积核`K`中的元素。将输入`X`逐行连结，得到长度为16的向量。然后将`W`与向量化的`X`做矩阵乘法，得到长度为4的向量。对其变形后，我们可以得到和上面卷积运算相同的结果。可见，我们在这个例子中使用矩阵乘法实现了卷积运算。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(\n",
       " [[[[348. 393.]\n",
       "    [528. 573.]]]]\n",
       " <NDArray 1x1x2x2 @cpu(0)>, \n",
       " [[1. 2. 3. 0. 4. 5. 6. 0. 7. 8. 9. 0. 0. 0. 0. 0.]\n",
       "  [0. 1. 2. 3. 0. 4. 5. 6. 0. 7. 8. 9. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 1. 2. 3. 0. 4. 5. 6. 0. 7. 8. 9. 0.]\n",
       "  [0. 0. 0. 0. 0. 1. 2. 3. 0. 4. 5. 6. 0. 7. 8. 9.]]\n",
       " <NDArray 4x16 @cpu(0)>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W, k = nd.zeros((4, 16)), nd.zeros(11)\n",
    "k[:3], k[4:7], k[8:] = K[0, 0, 0, :], K[0, 0, 1, :], K[0, 0, 2, :]\n",
    "W[0, 0:11], W[1, 1:12], W[2, 4:15], W[3, 5:16] = k, k, k, k\n",
    "nd.dot(W, X.reshape(16)).reshape((1, 1, 2, 2)), W"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在我们从矩阵乘法的角度来描述卷积运算。设输入向量为$\\boldsymbol{x}$，权重矩阵为$\\boldsymbol{W}$，卷积的前向计算函数的实现可以看作将函数输入乘以权重矩阵，并输出向量$\\boldsymbol{y} = \\boldsymbol{W}\\boldsymbol{x}$。我们知道，反向传播需要依据链式法则。由于$\\nabla_{\\boldsymbol{x}} \\boldsymbol{y} = \\boldsymbol{W}^\\top$，卷积的反向传播函数的实现可以看作将函数输入乘以转置后的权重矩阵$\\boldsymbol{W}^\\top$。而转置卷积层正好交换了卷积层的前向计算函数与反向传播函数：转置卷积层的这两个函数可以看作将函数输入向量分别乘以$\\boldsymbol{W}^\\top$和$\\boldsymbol{W}$。\n",
    "\n",
    "不难想象，转置卷积层可以用来交换卷积层输入和输出的形状。让我们继续用矩阵乘法描述卷积。设权重矩阵是形状为$4\\times16$的矩阵，对于长度为16的输入向量，卷积前向计算输出长度为4的向量。假如输入向量的长度为4，转置权重矩阵的形状为$16\\times4$，那么转置卷积层将输出长度为16的向量。在模型设计中，转置卷积层常用于将较小的特征图变换为更大的特征图。在全卷积网络中，当输入是高和宽较小的特征图时，转置卷积层可以用来将高和宽放大到输入图像的尺寸。\n",
    "\n",
    "我们来看一个例子。构造一个卷积层`conv`，并设输入`X`的形状为(1, 3, 64, 64)。卷积输出`Y`的通道数增加到10，但高和宽分别缩小了一半。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "3"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 10, 32, 32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv = nn.Conv2D(10, kernel_size=4, padding=1, strides=2)\n",
    "conv.initialize()\n",
    "\n",
    "X = nd.random.uniform(shape=(1, 3, 64, 64))\n",
    "Y = conv(X)\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们通过创建`Conv2DTranspose`实例来构造转置卷积层`conv_trans`。这里我们设`conv_trans`的卷积核形状、填充以及步幅与`conv`中的相同，并设输出通道数为3。当输入为卷积层`conv`的输出`Y`时，转置卷积层输出与卷积层输入的高和宽相同：转置卷积层将特征图的高和宽分别放大了2倍。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "4"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 3, 64, 64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv_trans = nn.Conv2DTranspose(3, kernel_size=4, padding=1, strides=2)\n",
    "conv_trans.initialize()\n",
    "conv_trans(Y).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在有些文献中，转置卷积也被称为分数步长卷积（fractionally-strided convolution）[2]。\n",
    "\n",
    "\n",
    "## 构造模型\n",
    "\n",
    "我们在这里给出全卷积网络模型最基本的设计。如图9.11所示，全卷积网络先使用卷积神经网络抽取图像特征，然后通过$1\\times 1$卷积层将通道数变换为类别个数，最后通过转置卷积层将特征图的高和宽变换为输入图像的尺寸。模型输出与输入图像的高和宽相同，并在空间位置上一一对应：最终输出的通道包含了该空间位置像素的类别预测。\n",
    "\n",
    "![全卷积网络](../img/fcn.svg)\n",
    "\n",
    "下面我们使用一个基于ImageNet数据集预训练的ResNet-18模型来抽取图像特征，并将该网络实例记为`pretrained_net`。可以看到，该模型成员变量`features`的最后两层分别是全局最大池化层`GlobalAvgPool2D`和样本变平层`Flatten`，而`output`模块包含了输出用的全连接层。全卷积网络不需要使用这些层。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "5"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(HybridSequential(\n",
       "   (0): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512)\n",
       "   (1): Activation(relu)\n",
       "   (2): GlobalAvgPool2D(size=(1, 1), stride=(1, 1), padding=(0, 0), ceil_mode=True, global_pool=True, pool_type=avg, layout=NCHW)\n",
       "   (3): Flatten\n",
       " ), Dense(512 -> 1000, linear))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretrained_net = model_zoo.vision.resnet18_v2(pretrained=True)\n",
    "pretrained_net.features[-4:], pretrained_net.output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们创建全卷积网络实例`net`。它复制了`pretrained_net`实例的成员变量`features`里除去最后两层的所有层以及预训练得到的模型参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "6"
    }
   },
   "outputs": [],
   "source": [
    "net = nn.HybridSequential()\n",
    "for layer in pretrained_net.features[:-2]:\n",
    "    net.add(layer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "给定高和宽分别为320和480的输入，`net`的前向计算将输入的高和宽减小至原来的$1/32$，即10和15。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "7"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 512, 10, 15)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = nd.random.uniform(shape=(1, 3, 320, 480))\n",
    "net(X).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来，我们通过$1\\times 1$卷积层将输出通道数变换为Pascal VOC2012数据集的类别个数21。最后，我们需要将特征图的高和宽放大32倍，从而变回输入图像的高和宽。回忆一下[“填充和步幅”](../chapter_convolutional-neural-networks/padding-and-strides.ipynb)一节中描述的卷积层输出形状的计算方法。由于$(320-64+16\\times2+32)/32=10$且$(480-64+16\\times2+32)/32=15$，我们构造一个步幅为32的转置卷积层，并将卷积核的高和宽设为64、填充设为16。不难发现，如果步幅为$s$、填充为$s/2$（假设$s/2$为整数）、卷积核的高和宽为$2s$，转置卷积核将输入的高和宽分别放大$s$倍。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "8"
    }
   },
   "outputs": [],
   "source": [
    "num_classes = 21\n",
    "net.add(nn.Conv2D(num_classes, kernel_size=1),\n",
    "        nn.Conv2DTranspose(num_classes, kernel_size=64, padding=16,\n",
    "                           strides=32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 初始化转置卷积层\n",
    "\n",
    "我们已经知道，转置卷积层可以放大特征图。在图像处理中，我们有时需要将图像放大，即上采样（upsample）。上采样的方法有很多，常用的有双线性插值。简单来说，为了得到输出图像在坐标$(x,y)$上的像素，先将该坐标映射到输入图像的坐标$(x',y')$，例如，根据输入与输出的尺寸之比来映射。映射后的$x'$和$y'$通常是实数。然后，在输入图像上找到与坐标$(x',y')$最近的4像素。最后，输出图像在坐标$(x,y)$上的像素依据输入图像上这4像素及其与$(x',y')$的相对距离来计算。双线性插值的上采样可以通过由以下`bilinear_kernel`函数构造的卷积核的转置卷积层来实现。限于篇幅，我们只给出`bilinear_kernel`函数的实现，不再讨论算法的原理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "9"
    }
   },
   "outputs": [],
   "source": [
    "def bilinear_kernel(in_channels, out_channels, kernel_size):\n",
    "    factor = (kernel_size + 1) // 2\n",
    "    if kernel_size % 2 == 1:\n",
    "        center = factor - 1\n",
    "    else:\n",
    "        center = factor - 0.5\n",
    "    og = np.ogrid[:kernel_size, :kernel_size]\n",
    "    filt = (1 - abs(og[0] - center) / factor) * \\\n",
    "           (1 - abs(og[1] - center) / factor)\n",
    "    weight = np.zeros((in_channels, out_channels, kernel_size, kernel_size),\n",
    "                      dtype='float32')\n",
    "    weight[range(in_channels), range(out_channels), :, :] = filt\n",
    "    return nd.array(weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们来实验一下用转置卷积层实现的双线性插值的上采样。构造一个将输入的高和宽放大2倍的转置卷积层，并将其卷积核用`bilinear_kernel`函数初始化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "11"
    }
   },
   "outputs": [],
   "source": [
    "conv_trans = nn.Conv2DTranspose(3, kernel_size=4, padding=1, strides=2)\n",
    "conv_trans.initialize(init.Constant(bilinear_kernel(3, 3, 4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读取图像`X`，将上采样的结果记作`Y`。为了打印图像，我们需要调整通道维的位置。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = image.imread('../img/catdog.jpg')\n",
    "X = img.astype('float32').transpose((2, 0, 1)).expand_dims(axis=0) / 255\n",
    "Y = conv_trans(X)\n",
    "out_img = Y[0].transpose((1, 2, 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，转置卷积层将图像的高和宽分别放大2倍。值得一提的是，除了坐标刻度不同，双线性插值放大的图像和[“目标检测和边界框”](bounding-box.ipynb)一节中打印出的原图看上去没什么两样。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input image shape: (561, 728, 3)\n",
      "output image shape: (1122, 1456, 3)\n"
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    },
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     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "d2l.set_figsize()\n",
    "print('input image shape:', img.shape)\n",
    "d2l.plt.imshow(img.asnumpy());\n",
    "print('output image shape:', out_img.shape)\n",
    "d2l.plt.imshow(out_img.asnumpy());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在全卷积网络中，我们将转置卷积层初始化为双线性插值的上采样。对于$1\\times 1$卷积层，我们采用Xavier随机初始化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "12"
    }
   },
   "outputs": [],
   "source": [
    "net[-1].initialize(init.Constant(bilinear_kernel(num_classes, num_classes,\n",
    "                                                 64)))\n",
    "net[-2].initialize(init=init.Xavier())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据集\n",
    "\n",
    "我们用上一节介绍的方法读取数据集。这里指定随机裁剪的输出图像的形状为$320\\times 480$：高和宽都可以被32整除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "13"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading ../data/VOCtrainval_11-May-2012.tar from http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 1114 examples\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 1078 examples\n"
     ]
    }
   ],
   "source": [
    "crop_size, batch_size, colormap2label = (320, 480), 32, nd.zeros(256**3)\n",
    "for i, cm in enumerate(d2l.VOC_COLORMAP):\n",
    "    colormap2label[(cm[0] * 256 + cm[1]) * 256 + cm[2]] = i\n",
    "voc_dir = d2l.download_voc_pascal(data_dir='../data')\n",
    "\n",
    "num_workers = 0 if sys.platform.startswith('win32') else 4\n",
    "train_iter = gdata.DataLoader(\n",
    "    d2l.VOCSegDataset(True, crop_size, voc_dir, colormap2label), batch_size,\n",
    "    shuffle=True, last_batch='discard', num_workers=num_workers)\n",
    "test_iter = gdata.DataLoader(\n",
    "    d2l.VOCSegDataset(False, crop_size, voc_dir, colormap2label), batch_size,\n",
    "    last_batch='discard', num_workers=num_workers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练模型\n",
    "\n",
    "现在可以开始训练模型了。这里的损失函数和准确率计算与图像分类中的并没有本质上的不同。因为我们使用转置卷积层的通道来预测像素的类别，所以在`SoftmaxCrossEntropyLoss`里指定了`axis=1`（通道维）选项。此外，模型基于每个像素的预测类别是否正确来计算准确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "12"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training on [gpu(0), gpu(1)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1, loss 1.3265, train acc 0.734, test acc 0.819, time 9.7 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 2, loss 0.5663, train acc 0.828, test acc 0.839, time 9.3 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 3, loss 0.4279, train acc 0.862, test acc 0.846, time 9.2 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 4, loss 0.3877, train acc 0.874, test acc 0.849, time 9.2 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 5, loss 0.3274, train acc 0.891, test acc 0.849, time 9.2 sec\n"
     ]
    }
   ],
   "source": [
    "ctx = d2l.try_all_gpus()\n",
    "loss = gloss.SoftmaxCrossEntropyLoss(axis=1)\n",
    "net.collect_params().reset_ctx(ctx)\n",
    "trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1,\n",
    "                                                      'wd': 1e-3})\n",
    "d2l.train(train_iter, test_iter, net, loss, trainer, ctx, num_epochs=5)"
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   "metadata": {},
   "source": [
    "## 预测像素类别\n",
    "\n",
    "在预测时，我们需要将输入图像在各个通道做标准化，并转成卷积神经网络所需要的四维输入格式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "13"
    }
   },
   "outputs": [],
   "source": [
    "def predict(img):\n",
    "    X = test_iter._dataset.normalize_image(img)\n",
    "    X = X.transpose((2, 0, 1)).expand_dims(axis=0)\n",
    "    pred = nd.argmax(net(X.as_in_context(ctx[0])), axis=1)\n",
    "    return pred.reshape((pred.shape[1], pred.shape[2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了可视化每个像素的预测类别，我们将预测类别映射回它们在数据集中的标注颜色。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "14"
    }
   },
   "outputs": [],
   "source": [
    "def label2image(pred):\n",
    "    colormap = nd.array(d2l.VOC_COLORMAP, ctx=ctx[0], dtype='uint8')\n",
    "    X = pred.astype('int32')\n",
    "    return colormap[X, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试数据集中的图像大小和形状各异。由于模型使用了步幅为32的转置卷积层，当输入图像的高或宽无法被32整除时，转置卷积层输出的高或宽会与输入图像的尺寸有偏差。为了解决这个问题，我们可以在图像中截取多块高和宽为32的整数倍的矩形区域，并分别对这些区域中的像素做前向计算。这些区域的并集需要完整覆盖输入图像。当一个像素被多个区域所覆盖时，它在不同区域前向计算中转置卷积层输出的平均值可以作为softmax运算的输入，从而预测类别。\n",
    "\n",
    "简单起见，我们只读取几张较大的测试图像，并从图像的左上角开始截取形状为$320\\times480$的区域：只有该区域用于预测。对于输入图像，我们先打印截取的区域，再打印预测结果，最后打印标注的类别。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "15"
    }
   },
   "outputs": [
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     "output_type": "display_data"
    }
   ],
   "source": [
    "test_images, test_labels = d2l.read_voc_images(is_train=False)\n",
    "n, imgs = 4, []\n",
    "for i in range(n):\n",
    "    crop_rect = (0, 0, 480, 320)\n",
    "    X = image.fixed_crop(test_images[i], *crop_rect)\n",
    "    pred = label2image(predict(X))\n",
    "    imgs += [X, pred, image.fixed_crop(test_labels[i], *crop_rect)]\n",
    "d2l.show_images(imgs[::3] + imgs[1::3] + imgs[2::3], 3, n);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 小结\n",
    "\n",
    "* 可以通过矩阵乘法来实现卷积运算。\n",
    "* 全卷积网络先使用卷积神经网络抽取图像特征，然后通过$1\\times 1$卷积层将通道数变换为类别个数，最后通过转置卷积层将特征图的高和宽变换为输入图像的尺寸，从而输出每个像素的类别。\n",
    "* 在全卷积网络中，可以将转置卷积层初始化为双线性插值的上采样。\n",
    "\n",
    "\n",
    "## 练习\n",
    "\n",
    "* 用矩阵乘法来实现卷积运算是否高效？为什么？\n",
    "* 如果将转置卷积层改用Xavier随机初始化，结果有什么变化？\n",
    "* 调节超参数，能进一步提升模型的精度吗？\n",
    "* 预测测试图像中所有像素的类别。\n",
    "* 全卷积网络的论文中还使用了卷积神经网络的某些中间层的输出 [1]。试着实现这个想法。\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## 参考文献\n",
    "\n",
    "[1] Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431-3440).\n",
    "\n",
    "[2] Dumoulin, V., & Visin, F. (2016). A guide to convolution arithmetic for deep learning. arXiv preprint arXiv:1603.07285.\n",
    "\n",
    "## 扫码直达[讨论区](https://discuss.gluon.ai/t/topic/3041)\n",
    "\n",
    "![](../img/qr_fcn.svg)"
   ]
  }
 ],
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